Despite substantial progress in managing uncertainty in software-intensive systems, existing methods often treat uncertainty sources independently and provide limited support for understanding their combined effects. When multiple uncertainties propagate through system elements and converge at shared variables, models, or decision points, they may interact in ways that alter system behavior, compromise requirements, or invalidate assurance arguments. This challenge, referred to as the uncertainty interaction problem, remains insufficiently understood. This roadmap paper reports the outcomes of the NII Shonan Seminar No. 232 on Uncertainty Interaction in Software-Intensive Systems (UNISON), held in March 2026. It develops a shared conceptual vocabulary for distinguishing uncertainty sources, propagation, confluence, interaction, and relevance; proposes an abstract workflow for identifying, filtering, and assessing relevant uncertainty interactions; and introduces a lifecycle-oriented framework for characterizing and selecting mitigation strategies. Building on these foundations, the paper organizes open challenges into a staged research roadmap spanning conceptual consolidation, reusable methods, engineering integration, validation, tooling, and community adoption. The roadmap provides a common reference point for researchers and practitioners working across software engineering, self-adaptive systems, control, artificial intelligence, formal methods, and assurance.
Javier Cámara, R. Mirandola, Kenji Tei et al.· ACM Transactions on Autonomo...· 0 citations
Large language models (LLMs) are increasingly used for model-to-code generation, but they weaken one of the central properties of model-driven engineering: traceability between design models and generated implementation artefacts. Existing approaches either rely on deterministic transformations, where trace links can be produced by construction, or treat traceability as a post-hoc recovery problem, where links are reconstructed after code has already been generated. This paper investigates a different possibility: preserving model-to-code traceability during LLM-based code generation. We study generation from UML class diagrams, represented in XMI, to Java and Python code. During generation, we capture token-level attribution between the model input and generated code output, and aggregate these signals into trace links between model elements and code features. We evaluate input masking, gradient-based attribution, and internal intervention techniques as mechanisms for preserving traceability during generation. Across open-weight LLMs of different sizes, value zeroing performs best, identifying the correct trace link with 90% top-1 accuracy and more than 95% top-5 accuracy. The results suggest that generating LLMs contain signals that can support meaningful model-to-code traceability, without requiring trace links to be reconstructed only after generation. This paper therefore reframes LLM-based model-to-code generation as a traceability-preservation problem. This opens a research direction for AI-supported MDE in which trace links are treated as engineering artefacts produced alongside generated code, rather than as evidence recovered afterwards.
Marc North, Nelly Bencomo, Amir Atapour-Abarghouei· Proceedings of the ACM/IEEE...· 0 citations
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